QuPrep — Quantum Data Preparation

The missing preprocessing layer between classical datasets and quantum computing frameworks.

QuPrep converts classical datasets into quantum-circuit-ready format. It is not a quantum computing framework, simulator, or training tool — it is the preprocessing step that feeds into Qiskit, PennyLane, Cirq, TKET, and any other quantum workflow.

CSV / DataFrame / NumPy / images / text / graphs  →  QuPrep  →  circuit-ready output

What QuPrep does

• Ingest tabular data, time series, images, text, and graphs — all in the same pipeline API
• Clean, normalize, and reduce dimensionality to fit your hardware qubit budget
• Encode data into circuits using 13 encoding methods (Angle, Amplitude, IQP, ZZFeatureMap, GraphState, and more)
• Recommend, compare, and auto-select the best encoding for your dataset and task
• Export circuits to 8 frameworks: OpenQASM 3.0, Qiskit, PennyLane, Cirq, TKET, Braket, Q#, IQM
• Formulate combinatorial optimization problems as QUBO / Ising models; export as QAOA circuit templates for your quantum framework

QuPrep does not train models, simulate circuits, run on quantum hardware, or optimize variational parameters.

Installation

pip install quprep

With optional extras:

# Framework exporters
pip install quprep[qiskit]     # Qiskit QuantumCircuit
pip install quprep[pennylane]  # PennyLane QNode
pip install quprep[cirq]       # Cirq Circuit
pip install quprep[tket]       # TKET/pytket Circuit
pip install quprep[braket]     # Amazon Braket Circuit
pip install quprep[qsharp]     # Q# / Azure Quantum
pip install quprep[iqm]        # IQM native format
pip install quprep[frameworks] # all framework exporters at once

# Data modalities
pip install quprep[image]      # image ingestion (Pillow)
pip install quprep[text]       # text embeddings (sentence-transformers, ~2 GB)
pip install quprep[modalities] # image + text at once

# Other
pip install quprep[umap]       # UMAP dimensionality reduction
pip install quprep[viz]        # matplotlib circuit diagrams
pip install quprep[all]        # everything

Requirements: Python ≥ 3.10. Core dependencies: numpy, scipy, pandas, scikit-learn.

Quickstart

One-liner

import quprep as qd

result = qd.prepare("data.csv", encoding="angle", framework="qasm")
print(result.circuit)

Full pipeline

import quprep as qd

pipeline = qd.Pipeline(
    cleaner=qd.Imputer(),
    reducer=qd.PCAReducer(n_components=8),
    encoder=qd.IQPEncoder(reps=2),
    exporter=qd.PennyLaneExporter(),   # pip install quprep[pennylane]
)
result = pipeline.fit_transform("data.csv")
qnode = result.circuit   # callable qml.QNode

Data modalities — time series, images, text, graphs

import quprep as qd

# Time series — sliding window then encode
from quprep.ingest.time_series_ingester import TimeSeriesIngester
from quprep.clean.window_transformer import WindowTransformer

result = qd.Pipeline(
    preprocessor=WindowTransformer(window_size=5, step=1),
    encoder=qd.AngleEncoder(),
).fit_transform(TimeSeriesIngester(time_column="date").load("sensor.csv"))

# Images — pip install quprep[image]
from quprep.ingest.image_ingester import ImageIngester
result = qd.prepare("images/", encoding="angle", ingester=ImageIngester(size=(8, 8), grayscale=True))

# Text — TF-IDF (no deps) or sentence-transformers (pip install quprep[text])
from quprep.ingest.text_ingester import TextIngester
texts = ["quantum computing is powerful", "machine learning meets QML", ...]
result = qd.prepare(texts, encoding="angle", ingester=TextIngester(method="tfidf", max_features=16))

# Graphs — lossless graph state encoding
from quprep.ingest.graph_ingester import GraphIngester
from quprep.encode.graph_state import GraphStateEncoder
import numpy as np
graph_list = [np.array([[0,1,1],[1,0,0],[1,0,0]], dtype=float), ...]  # adjacency matrices
result = qd.Pipeline(encoder=GraphStateEncoder()).fit_transform(
    GraphIngester(features="adjacency").load(graph_list)
)

More features

Supported encodings

Supported export frameworks

Documentation

Full documentation at docs.quprep.org

• Installation
• Quickstart guide
• Encoding guide
• API reference

Examples

Tutorials — beginner-friendly, end-to-end walkthroughs:

Topic	Launch
Your first quantum-ready dataset
Real-world messy data (NaN, outliers, imbalance)
End-to-end with a framework (audit → suggest → verify → export)

How-to guides — task-focused, one topic each:

Task	Launch
Choose an encoder
Export to Qiskit / PennyLane / Cirq / Braket
Inspect a circuit (angles, gates, diagrams)
Load external data (CSV, OpenML, HuggingFace, streaming)
Non-tabular data (time series, sparse, multi-label)
Fix class imbalance (SMOTE / oversample / undersample)
Validate before encoding (report, compatibility, schema)
Detect data drift in production
Assess encoding quality (expressibility, barren plateau)
Noise-aware preprocessing for NISQ hardware
Solve a QUBO / Ising problem (Max-Cut, Knapsack, QAOA)
Write a custom encoder and register it

Contributing

Contributions are welcome. Please read CONTRIBUTING.md before opening a pull request.

• Open an issue for bugs or feature requests
• Start a discussion for questions or ideas

License

Apache 2.0 — see LICENSE.

Citation

If you use QuPrep in your research, please cite:

@software{quprep2026,
  author    = {Perera, Hasarindu},
  title     = {QuPrep: Quantum Data Preparation},
  year      = {2026},
  publisher = {Zenodo},
  version   = {0.10.0},
  doi       = {10.5281/zenodo.19286258},
  url       = {https://doi.org/10.5281/zenodo.19286258},
  license   = {Apache-2.0},
}